Adaptive clustering with artificial ants


  • Diego Alejandro Ingaramo Lab. de Investigación y Desarrollo en Inteligencia Computacional (LIDIC), Universidad Nacional de San Luis, San Luis, Argentina
  • Mario Guillermo Leguizamón Lab. de Investigación y Desarrollo en Inteligencia Computacional (LIDIC), Universidad Nacional de San Luis, San Luis, Argentina
  • Marcelo Luis Errecalde Lab. de Investigación y Desarrollo en Inteligencia Computacional (LIDIC), Universidad Nacional de San Luis, San Luis, Argentina


computational intelligence, bioinspired algorithms, clustering, data mining


Clustering task aims at the unsupervised classification of patterns (e.g., observations, data, vec- tors, etc.) in different groups. Clustering problem has been approached from different disciplines during the last years. Although have been proposed different alternatives to cope with clustering, there also exists an interesting and novel field of research from which different bioinspired algorithms have emerged, e.g., genetic algorithms and ant colony algorithms. In this article we pro- pose an extension of the AntTree algorithm, an example of an algorithm recently proposed for a data mining task which is designed following the principle of self-assembling behavior observed in some species of real ants. The extension proposed called Adaptive-AntTree (AAT for short) represents a more flexible version of the original one. The ants in AAT are able of changing the assigned position in previous iterations in the tree under construction. As a consequence, this new algorithm builds an adaptive hierarchical cluster which changes over the run in order to improve the final result. The AAT performance is experimentally analyzed and compared against AntTree and K-means which is one of the more popular and referenced clustering algorithm.


Download data is not yet available.


[1] N. Slimane, N. Monmarché, and G. Venturini. Antclass: discovery of clusters in numeric data by an hybridization of an ant colony with kmeans algorithm. Rapport interne 213, Laboratoired ’ Informatique del ’ Universit e de Tours, E3i Tours,, 1999.
[2] H. Azzag, N. Monmarche, M. Slimane, G. Venturini, and C. Guinot. Anttree: A new model for clustering with artificial ants. In Ruhul Sarker, Robert Reynolds, Hussein Abbass, Kay Chen Tan, Bob McKay, Daryl Essam, and Tom Gedeon, editors, Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, pages 2642–2647, Canberra, 8-12 December 2003. IEEE Press.
[3] N. Labroche, N. Monmarch´e, and G. Venturini. AntClust: Ant Clustering and Web Usage Mining. In Genetic and Evolutionary Computation Conference, pages 25–36, Chicago, 2003.
[4] Cheng-Fa Tsai, Chun-Wei Tsai, Han-Chang Wu, and Tzer Yang. Acodf: a novel data clustering approach for data mining in large databases. J. Syst. Softw., 73(1):133–145, 2004.
[5] Alex A. Freitas. A survey of evolutionary algorithms for data mining and knowledge discovery. In A Ghosh and S Tsutsui, editors, Advances in Evolutionary Computation, pages 819–845. Springer-Verlag, August 2002.
[6] Hussein Abbass, Charles Newton, and Ruhul Sarker. Data Mining: A Heuristic Approach. Idea Group Publishing, Hershey, PA, USA, 2002.
[7] Marco Dorigo and Luca Maria Gambardella. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evolutionary Computation, 1(1):53–66, 1997.
[8] V.K. Jayaraman P.S. Shelokar and B.D. Kulkarni. An ant colony approach for clustering. Technical report, Chemical Engineering and Process Division, National Chemical Laboratory, India, 2003.
[9] F. Azuaje N. Bolshakova. Improving expression data mining through cluster validation. 2003.
[10] Maria Halkidi, Yannis Batistakis, and Michalis Vazirgiannis. Clustering validity checking methods: Part II. SIGMOD Record, 31(3):19–27, 2002.
[11] Ian H. Witten and Eibe Frank. Data mining: practical machine learning tools and techniques with Java implementations. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2000.
[12] C. L. Blake and C. J. Merz. UCI repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences,∼mlearn/MLRepository.html, 1998.




How to Cite

Ingaramo, D. A., Leguizamón, M. G., & Errecalde, M. L. (2005). Adaptive clustering with artificial ants. Journal of Computer Science and Technology, 5(04), p. 264–271. Retrieved from



Original Articles

Most read articles by the same author(s)